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Fertiliser and food policy in LDCs
Fertiliser and food policy in LDCs
C. Peter Timmer
At
a time
high
of fertiliser
prices
more
about
the
relationships food
are
prices
for
dilemma ting
to
the these and
Impact attention
of
Peter
Professor of
University,
poses
food
of a simple
price
a
attempproduction Through
are
discussed they
receives of
its
special frequen;
Timmer
is
of
Economics,
Nutritional Ithaca,
H.E.
Sciences, New
York,
Babcock DiviCornell 14850,
USA.
’ Christina Crisostomo David, A Model of Fertilizer Demand of Asian Rice Farms: A Micro-Mac0 Analysis, Unpublished PhD Dissertation, Stanford University.
1975. ‘The countries were Japan, South Korea, Taiwan, (West) Malaysia, Sri Lanka, Indonesia, Thailand, Philippines, Burma, India, and Pakistan-Bangladesh. The addition of India and Pakistan-Bangladesh plus a much longer time period enabled David to achieve better results than Timmer and Falcon reported for a similar model. See C. Peter Timmer and Walter P. Falcon, ‘The political economy of rice production and trade in Asia,’ in Agriculture in Development Theory, Lloyd Reynolds led), Yale University Press, 1975 and ‘The impact of price on rice trade in Asia’, in Agriculture, Trade and Development George Tolley, (ed). Ballinger Press, 1975.
FOOD POLICY
February
Fertiliser to food
offer.
instrument.
Food
Fertiliser usage is a critical determinant of food production and an understanding of the factors affecting the level of fertiliser application on food crops is essential to an understanding of the global food situation. It is no simple task because interrelationships among fertiliser use, food supplies, and prices are complex and multidirectional. Food and fertiliser policies have frequently ignored all but the most obvious of these relationships and consequently have often been counterproductive in the long and sometimes even in the shortterm.
macro-model
insights
because
use as a policy
sion
which
rising prices.
policy
in-
in increased
relationships the
and
price
governments
increase
use
understood
Fertiliser
food
for
and
to know
fertiliser
reflected
and minimise
C.
poorly
between
production.
creases
scarcity
it is important
1976
Four separate categories of fertiliser to food conversion rates must be considered: short-term versus long-term and micro-functions versus macro-functions. Short-term functions can be either micro or macro and refer to food grain yield increments achieved by fertiliser increments holding constant all or most other factors of production. In particular, short-term responses assume no changes in plant varieties, water control, and general cultural practices. Long-term responses refer to the food grain yield increment achieved by fertiliser increments when all or most other factors of production have also adapted to the new fertiliser levels. Naturally, long-term responses are expected to be significantly larger than short-term responses. The distinction between micro- and macro-functions refers to the level of the response observation. Small plot trials for example, in farmers’ fields or in experimental stations, yield the data used to calculate micro-functions. Aggregate data on yields and fertiliser use by regions or even countries are used to calculate macro-response functions. Clearly, a continuity exists between micro and macro and between short-term and long-term, but the distinctions are both conceptually and empirically useful. Current work by Christina Crisostomo David for the Stanford Rice Project’ not only illustrates the distinctions nicely but also provides extremely important quantitative parameters that are an essential part of both the analytical and policy discussions that follow. The results hold specifically for the Asian rice economy (excluding China), but the issues are general. David examined data for rice production, area harvested, and fertiliser consumption for 11 countries* for the years 1950 to 1972. A 143
Fertiliser arldfood policy it1L DCs
simple Cobb-Douglas
(log linear) production
function of the form
R = AiHT’, where
R = Rice production H = Area harvested, F = Fertiliser nutrients applied, and Ai, a, b = Estimated parameters,3 showed the following results: R = 1.09 Ho.859 F0-143 (R2 = 0.946) R = A,I HI.444
3Ai is the intercept term for the function, where i refers to the country-specific intercept. Alternatively a single intercept for all countries can also be estimated as in (1). Parameters a and b are the production elasticities ot area and fertiliser respectively. 4This result is nearly identical to the Timmer-Falcon findings although the absolute magnitudes here are significantly lower, probably reflecting the longer time period in the sample before fertiliser responsive varieties were widely available. Further discussion of the logic of this model and more detailed results are contained in the works cited in footnotes 1 and 2. 5The six countries were the Philippines, India. Pakistan, Indonesia. West Malaysia, and 7.hailand. See David, op cit. for details of village locations, environment and so on.
144
F0.073
(1)
(RZ = 0.99 1)
(2)
Both (1) and (2) are macro-functions because they are estimated from national aggregate data. Equation (1) can be interpreted as a long-term function, however, and equation (2) as a short-term function. The difference is that in (1) a single function is estimated for all eleven countries in the sample - the assumption being that in the long-term all countries can invest in water control projects, suitable plant varieties, and cultural practices to enable the countries with below average yields to achieve the yields reached by the above average countries. In this long-term environment the response elasticity of rice production to higher fertiliser levels is O-143, ie, a 10% increase in fertiliser application would increase rice production by 1.43% with the same area harvested. Equation (2) permits each country in the sample to have its own intercept term (not shown except as Ai). The production elasticities are still constrained to be the same for all countries, but the starting point (yield with zero fertiliser) is unique to each country. This permits the fertiliser response to relate to each country’s specific environment, and thus the estimated parameter can be interpreted as a short-term response. Equation (2) shows that this short-term response is about half that of the long-term response.4 In short, yield response to fertiliser in the long term after environmental, varietal, and cultural changes are also forthcoming is about twice as large as the response in the short term. The variation between micro- and macro-functions is equally dramatic. Because of the relative continuity in aggregation from field plots all the way to supra-national (eg, ‘Asian’) boundaries, the distinction can be illustrated at a number of levels. For example, in the macro model shown in (1) and (2) country-specific fertiliser response elasticities can be estimated as well as country-specific intercepts. These range from 0.02 in Burma to O-43 in Sri Lanka and Taiwan while the mean for the sample with separate intercepts, shown in (2) was 0.07. Obviously very substantial differences exist, even at national aggregate levels, between physical response rates to changing fertiliser applications. Not surprisingly, the differences become even more dramatic at lower levels. David was able to estimate the same production function model shown in (1) and (2) for a set of Asian farm level data from 33 villages in six countries for the wet season of 197 1/72.5 The results are shown in Equations (3) and (4). R = 1.2 HO-813 ~0.124 (R2 = 0.754) (3) R =A ,Jf0'837 F0.095 (R2 = 0.862) I (4)
FOOD POLICY
February
1976
Fertiliser
’
The data are drawn from FAO, ‘Statistics of crop responses to fertilisers’. Freedom from Hunger Campaign Fertilizer Program, Rome, various years.
Country
FOOD POLICY
February
1976
policy in L DCs
Once again, (3) and (4) show the expected decrease in fertiliser response, from O-124 to O-095, from long-term to short-term. The decrease is not so dramatic as before because the sample refers specifically to the wet season crop only. The villages studied were selected on the basis of good water control, and modern varieties were extensively used (except in Thailand). Consequently the potential difference in environmental varieties and cultural practices is not as wide as in the national data set, and so the short-term and long-term fertiliser responses are closer. When separate fertiliser response elasticities are estimated, they range from 0.016 in Sidomulyo, East Java, Indonesia, to 0.386 in Mahipon, Geipan, Nueva Ecija, the Philippines. Within the Philippines, for example, the range is 0.05 to 0.386. A further illustration of the wide variation in fertiliser response to be expected from site to site is given in Table 1. These data6 still refer only to rice, and yet the nitrogen response alone varies from 4.7 kg of rice per kilogram of nitrogen in Burma to 10.5 kg in Bangladesh. Statistics for other crops show wider variations because the environments tend to vary more than the Asian rice environment. Where does this leave us in undersatnding the response of food grain to fertiliser? For rice, FAO has reported a relationship on a worldwide basis between increases in yield of paddy and applied nitrogen.’ On the basis of data from 385 samples in 20 countries, a linear response function showed a 12-13 kg increase in paddy production for every kilogram increase in nitrogen. This high response rate should be considered as a long-term potential due to the pooled nature of the data. It offers little direct aid to a policy-maker concerned about short-term response in a particular environment. For this, attention must be turned to the type of results produced by David, though these must be used with caution because of the variation she observes from village to village and from year to year (in a third sample not discussed here). A substantial analytical understanding, however, has been achieved at the macro-level about differences between short-term and longterm responses. Since a developing nation’s food policy must appropriately balance polices to achieve short-term needs and those designed for longer-term goals, this understanding is critical to the subsequent discussion of food and fertiliser policy alternatives.
Table 1. Rice yield responses from fertiliser
a Computed from the report, Statistics of Crop Responses from Fertilizer. Food and Agriculture Organization, Rome 1966. Increases in yields are those resulting from application of 30 kg of each plant nutrient per hectare except in the case of South Korea where yield increases are those resulting from 60 kg.
andfood
Burma Sri Lanka Bangladesh Ghana India Iran Thailand Vtet-Nam South Korea
Yield per hectare without fertiliser
applications,
selected countriesa
Increase in yield per kilogram
Nitrogen
Phosphate
Kg
Kg
Kg
1432 1476 991 749 1230 2049 1172 1271 2350
4.7 5.2 1IO.5 9.1 9.9 7.6 9.0 5.4 8.0
2.6 5.4 7.1 9.1 6.5 8.2 8.5 3.0 0.5
of
Potash Kg 1 .o 1 .o 3.3 6.1 _ 1 .2 3.2 0.7 2.3
145
Fertiliser andfood policy in LDCs
Farmers’ use of fertiliser The factors affecting any farmer’s fertiliser use can be conveniently grouped into three broad categories: 1. environmental factors, especially the physical response of the crop to fertiliser, 2. economic factors, especially the price of fertiliser relative to the price for which the crop can be sold but also including any capital or credit constraints on how much fertiliser can be purchased, and 3. the conditions of knowledge about fertiliser, the degree of uncertainty surrounding the results of its use, and the attitude about attendant risks.8
’ FAO. The Response of Rice to Fertilizer. Rome, 1966. ’ Each of these categories is a major field of research and published findings. The interested reader will find further discussion and bibliographic references in the works cited in other footnotes. ’ Zvi Griliches. ‘The demand for fertilizer: an economic interpretation of a technical change’, Journal of Farm Economics, August 1958 and ‘Distributed lags, disaggregation, and regional demand functions for fertilizer’, Journal of Farm Economics, February 1959. ” Griliches calculated that about 80% of the adjustment takes place in five years.
GRILICHES’
MODEL
Let barred the relevant
letters denote logarithms variables. Then
?; = c where
F;
+
of
d Pft + et,
= desired fertiliser consumption in long-term equilibrium,
Pfr = price of fertiliser at time t relative to the price of agricultural output at time t, and
e*
146
= a random term.
disturbance
The primary concern here is to delineate those factors susceptible to influence by government policy and to sense the quantitative significance and flexibility of each. The issues can best be understood in the context of what has become the standard model used to explain the dynamics of fertiliser consumption, the lagged adjustment model first used for this purpose by Zvi Griliches.’ (see below) Griliches’ result for US demand for total fertiliser nutrients over the 19 11-1956 period indicated a highly significant, short-term elasticity of about -0.5 and an adjustment coefficient of about O-25. Taken together, these two coefficients imply a long-term elasticity of about -2.0. That is, if fertiliser prices increase by 10% (with crop prices constant), then fertiliser use drops by about 5% in the first year and by about 20% after the price increase has been maintained for a number of years.‘O Because the model requires a time series of observations on fertiliser use and price, it has been applied to only a few developing countries. Table 2 summarises those studies that have come to light so far. Although the elasticity and adjustment coefficients do not seem consistent at first glance, a number of the more extreme values are explained by fairly obvious statistical problems in data and specification. With this proviso, a fair summary would be that in developing countries for which studies have been done, the short-term price elasticity of demand for fertiliser is about -0.5 to -1.0, and the long-term elasticity, assuming the new relative prices are maintained,
The relative prices of other important inputs should also enter Equation (5). but they were not significant in Griliches’ work and are omitted for simplicity. The use of relative price implies an equal farmer adjustment to a one percent rise in output price or to a one percent fall in fertiliser price. The validity of this assumption seems to vary from developed to less developed agricultures, with enormous implications for government pricing policies. The issue is re-examined in the last section. The amount of fertiliser actually used at time t is equal to the desired (or appropriate) amount only in long-term equilibrium. The adjustment of actual towards desired use is through proportional changes, as in Equation (6). Fr - Ft-, = r (et* - Ft-lI, (6)
where
Ft
r
= actual fertiliser consumption in time t. and = the adjustment coefficient (O
The model is completed ey substituting (5) into (6) and solving for Ft, as follows: ~t=~+drPft+(l-rlFt_,+ret.
(7)
With appropriate statistical precautions taken with regard to autocorrelation of the residuals, Equation (7) is suitable for direct estimation. Since the variables are in logarithms, the short-term elasticity of demand for fertiliser with respect to its relative price is given by the estimated and the long-term coefficient dr, elasticity, after all adjustments have been made, is given by d.
FOOD POLICY
February
1976
Fertiliser and food policy in L DCs Table 2. Summary
of fertiliser
demand studies in developing Elasticity
Country
Time period
Short-term
Brazil
1949-71
-1.12b
India
1953/4-67/B 195819-6314
937
of demanda Long-term
Comments OLS estimate with area cultivated included in equation. Real average price of all fertiliser, significant autocorrelation. Same as traditronal model but no autocorrelation using dynamic model.
-o.33c
-1.94
-0.31d -0,530 -I .2d
-0.34 -6.63 -2.5b
_
-0.74b
Price elasticity estrmated by OLS for total fertiliser use utilising five years averages as observations. Thus the elasticity is more long-term than short-term in nature.
-0.88
Lagged adjustment total fertrliser paid OLS estimate from Equation contains
1883-l
Korea
1960-7 1
-0.17
1971
-0.7Ob
_
Philippines
1958-72
0.59b
_
Taiwan
1950-66
-o.55c
_
-2.03b
a Demand elasticity for all nutrients unless otherwise noted. b Denotes significance at 0.9 or higher. c Denotes significance between 0.8 and 0.9. d Denotes significance between 0.7 and 0.8. Source: C. Peter Timmer. ‘The demand for fertilizer in developing countries’, Food Research institute Studies. Vol XIII. No 3. 1974.
” A much fuller treatment of these results is contained in C. Peter Timmer, ‘The demand for fertilizer in developing countries’, Food Research Institute Studies, Vol XIII, No 3, 1974. “Wallace Huffman,‘Decision-making:the role of education’, American Journal of AgriculturalEconomics, February 1974. l3 Timmer, 1974, op cit.
February
Adjustment coefficient
_
Japan
FOOD POLICY
countries
1976
-2.99
0.92 0.08 0.50
_
0.68
Demand function for nitrogen fertiliser contains area irrigated Same, but equation excludes area irrigated. Analysis of covariance results for nrtrogen consumption using Indian state data. The short-term response is from an equation that includes separate state intercepts; the long-term elasticity excludes them. The adjustment coefficient is calculated from the two responses.
model using deflated price index of by farm. cross-section survey of 300 farmers. many other farm specific variables.
OLS: Equation includes sugar and corn hectarage and rice yield. Price elasticity for CIF nitrogen value deflated by consumer price index. Demand function for nitrogen using relative price of rice to nitrogen. Equation contains lagged yield which is highly significant. Dynamic equation excludes lagged yields but price and fertiliser variables the same.
is in the range of -1.5 to about -3-O. Naturally these results must be used with great caution, but they do show a widespread tendency for farmers in developing countries to behave ‘appropriately’ with respect to fertiliser prices.” In Griliches’ simple dynamic adjustment model the adjustment coefficient (r) serves to capture the impact of all non-price variables that have an impact on the level of fertiliser use. Thus, although capital availability, knowledge, and (flexible) environmental factors may be limiting in the short-term, their impact in this model is through the rate of adjustment and not through the level of long-term demand. A functional specification of r would be a major improvement in this model because it would permit separate quantitative estimates of the impact of these other factors that are presently swept into a single effect. W. Huffman has constructed a model for this purpose to test the effect of education on the rate at which farmers adjust fertiliser use on corn due to changes in fertiliser prices.” Extension of his model to include other obvious variables income or assets, length of experience, extent of knowledge, types of crops, etc - would be a major contribution. For developing countries with limited experience in the use of fertiliser some of these variables should also appear directly in the long-term function. As I showed in an earlier article a dynamic adjustment model is still entirely appropriate in this context.13 In the second stage of her analysis, David tackles these types of questions directly. Her goal is to measure the separate impact of price and environmental variables on the demand for fertiliser. To do so, she uses the country (or village) specific intercept and fertiliser 147
Fertiliser audfood
poliq,
in idDCs
elasticities estimated in the production functions discussed earlier as semi-continuous variables along with relative fertiliser price in a loglinear demand function. Despite the apparent obviousness of this approach14 David appears to be the first to use it successfully. Her results are important both for the actual magnitudes and their amazing uniformity between macro- and micro-functions. They are summarised in Table 3. The price elasticities shown in Table 3 do not correspond directly to the long- and short-term elasticities shown in Table 2 because David’s estimates are based on a simple log-linear model rather than the dynamic adjustment model (because the data are mostly cross-sectional). Still, there are short-term and long-term implications in these results that are important.
” For instance, the fertiliser demand function derived from the profit-maximising conditions applied to a Cobb-Douglas production function contains just these terms.
Equations (7) and (10) give the simplest possible fertiliser demand function with no attempt to hold environment or varieties constant in the analysis. The result is a price elasticity of about -0.9 for both the macro and micro data. Assuming that all environmental and varietal adaptions had already been made to take advantage of optional fertiliser use then the two estimates provide a reasonable longer-term price elasticity of demand. However, since we are probably dealing with situations where this perfect adaption has not yet occurred and when there will be lags in achieving it, the model underestimates the true long-term elasticity which would apply assuming there had been perfect adaption in all respects. As increasing attention is paid to environmental differences, and then to varietal difference, ie, in (8), (9) and (1 I), (12), the estimated price elasticity drops significantly. In the macro-function, where environmental and varietal differences among countries are quite substantial, the elasticity drops to -0.5 when production function intercepts and output elasticities are entered and to -0.3 when differences in use of modern varieties are considered. Thus the shortterm price response of Asian farmers to fertiliser price changes is about -0.5 when environment is not permitted to change and about -0.3 when neither environment nor varities can change. As expected, the micro-function shows less dramatic changes in price elasticity with added environmental variables because the environment does not differ nearly so much as in the macro-sample Table 3. Logarithmic
fertiliser
Equation no.
Fertiliserrice price
demand functions Production
a These data are from the set of 188 observations used to estimate Equations (I) and (2). The fertiliser data were roughly adjusted for amounts used on non-rice crops. b These data are from the set of over 2400 observations used to estimate Equations (31 and (4). ‘Modern varieties’ refers to the proportion of area planted to high-yielding modern varieties. Note:
Figures in parentheses
are f-values.
Source: Cristina Crisostoma David, A Model of Fertiiker Demand of Asian Rice Farms: A Micro-Macro Analysis, Unpublished PhD dissertation, Stanford University, 1975.
Intercept
“Adjusted” (7)
1.874
(8)
1.639
(91
0,945
-0~922 t-4.056) -0.510 (-3-472) -0.320 1-2-989)
Intercept
2.035
(11)
1.562
112)
1.527
-0.863 (7.874) -0.691 b-6.245) -0.650 (-5.529)
Modern varieties
R‘J
Aggregate Asian Data, 1950.1972a
Data for 33 Selected (IO)
function output elasticity
-
-
0.084
0.855
7.334
-
0.639
(3.606) 0.267 (1.506)
(7,677) 3.705 (4.959)
_
0.864 112.7481
0.812
Asian Villages, Wet Season Ig70/7Ib _ 0,584 (7-368) 0.580 (7.307)
0.170 2.326 68.7393 2.336 (8.768)
-
0.038 (0.037)
FOOD POLICY
0.252 0.253
February
1976
Fertiliser andfood polic)! it1L DCs
the villages were chosen for their relative homogeneity in water control and so on. Thus the coefficient attached to the output elasticity is uniformly smaller (but not’less significant due to the larger sample) than in the macro-sample. Further, the ‘modern varieties’ coefficient is not at all significant in the micro-function, reflecting the lack of variation. The ultimate result is a fairly high confidence in a short-term price elasticity at the micro-level of about -0.6. Which is more important in determining fertiliser demand - price or environment? On the basis of her entire analysis, the depth of which has only been hinted at here, David judges that roughly onethird of the explained variation in fertiliser use is accounted for by price differences and the remaining two-thirds by the environmental and varietal factors. This leaves unanswered the determinants of the flexibility of these environmental variables and the costs of changing them. David also points out that changes in environment and varieties may be price-incentive related, as indicated above. One last empirical issue is also left dangling. The models and results so far have assumed an independence between fertiliser prices and output prices, an independence which seems natural in view of economists’ normal models of short-term farm decision-making. But the desirability of having both short-term and long-term macroparameters for policy planning at national and international levels calls into question this mode1 and the results obtained from it.
Long-term fertiliser demand In the short term, farmers assume that the prices of their inputs and expected prices of their output are fixed and not subject to influence by individual decisions about input and output combinations and levels. But in the long term, prices in food grain markets depend on food grain supplies available relative to demand. The level of these supplies from domestic and international sources depends at least partially on the use of fertiliser in the production of the commodity. The interrelationships between food demand functions and production functions that determine the longer-term equilibrium at the macro-market level also in turn have significant implications for the long-term response of farmers to change in the price of fertiliser and then for the level of food prices relative to fertiliser prices. A simple long-term macro-mode1 for fertiliser demand (see over) illustrates these points. The mode1 shows that striking differences exist between short-term response elasticities to fertiliser price changes and long-term elasticities. Using David’s value of 0.14 for the response elasticity of food grain production in the model the short-term fertiliser demand elasticity is -1.16, and the food grain supply elasticity is -0.16. The comparable long-term elasticities are -0.64 and -0.09 respectively. These long term values are only about halfthe short-term values, a result just the reverse of what was found for short-term versus longterm micro-functions (see Table 2). The source of the paradox lies in the aggregation from micro to macro and is a familiar issue to macro-economists. The driving mechanism of the fertiliser demand macro-mode1 is the link between food grain prices and supplies forthcoming from the production function. This link is entirely missing in a micro-context. In the simplest version of the mode1 used here food grain supplies depend FOOD POLICY
February
1976
Fertiliser audfoodpoliq LONG-TERM This section
DEMAND summarises
in LDCs b,h a
originally presented in a fertiliser demand context (see footnote 1 1 above) and later extended to more general food-energy relationships.* The interested reader is referred to these two articles for details of the models and fuller discussion of varieties and implications. The model is built from an aggregate production function of the sort estimated empirically in Equation (I) and an aggregate consumption function for one of the major food grains. Since our primary interest is in fertiliser and the food grain price, the model subsumes other important variables into the intercept terms.
When farmers treat the price of grain, PO, and the price of fertliser, 4, as given in the short-term, the assumption of profit maximisation applied to the production function yields a short-term demand function for fertiliser and consequently a short-term supply function for food grains.
FX sr al’s,sr
Q,
=A,Fb
= Ff fA,,b)
= f21As.b)
where
Qs,Qd
= AdPoh
Pf
Pf
f b-l
___b b-l
Po
Po
I l-b
(151
- b l-b
(16)
(13) where
ad
.T response elasticities of food grain production and consumption respectively.
model
FTr
(14)
= quantity of food grains produced and consumed in a given year,
F
= fertiliser
appiications
As
= an intercept term that captures the effect of other important production inputs such as water, labour, etc
Q$,.
fi f‘A,,b)
= the short-term equilibrium input of fertiliser food into grain production, assuming profit maximisation, short-term -the equilibrium level of food grain supplies, and = functions of coefficients in the production function
PO = the price of food grain Ad
= an intercept term that captures the effect of other important demand factors such as population inand come, and
The equilibrium price of food grain in this model, PO”,can be found by equating the demand and supply functions and solving.
AdPoh P,
* C. Peter Timmer. ‘Interaction of energy and food prices in less developed countries’, American Journal of Agricultural Economics, May 1975.
(171
= AsFb = f$A,Ad
$-
(16)
The solution value is used in Equations 15 and 16 to find the long-term equilibrium values of fertiliser use and food grain supplies as a function of fertiliser price.
5: =fir’As,
Ad, h, bf P+‘--i7
(19)
hb as*,,. = fs (As, Ad, h, 6) Pfb+h’b-‘)
I
where
F* ii
0”
s,ir
(213)
= the long-term equilibrium level of fertiliser use in food grain productions, and
= the
long-term equilibrium level of food grain supplies.
Striking differences exist between the short-term elasticities to response fertiliser price changes in Equations (15) and (16) and the long-term elasticities in Equations (19) and (20). If b = 0.14, the value in David’s long-term macro-function shown in Equation( 11, then the short-term fertiliser
demand
elasticity
(,A,)
is -1.16,
and the food grain supply elasticity
(,A+
is -0.16. Happily, this fertiliser demand elasticity calculated from David’s longerterm physical response elasticity of 0.14 coincides fairly well with her own estimated demand elasticity of about -0.9 in her macro function (Table 3). The time perspective of the two models is different so that ‘long-term’ in her functions refers to changes in variety and environment but does not correspond to long-term market equilibrium in the macro-model under discussion here. The comparable long-term equilibrium responses of fertiliser use and food grain supplies to changes in fertiliser
pricesarefbw, h
)and (hbIThat b+hlb- 1) is, the degree of price response from the food grain consumption fbnction, h, is an important parameter in determining the elasticities to long-term response changes in fertiliser prices. If b = 0.14 as before, and h = -0.2. a reasonable value for a basic food grain with few subthe stitutes, respective long-term elasticities are -0.64 and -0.09.
entirely on the level of fertiliser application. (Other factors are held constant in the intercept term.) When the price of fertiliser rises exogenously, as when the raw feed stocks for fertiliser plants become scarce or shipping costs increase, the short-term effect is to cut back fertiliser applications according to the short-term elasticity (&). But this has a subsequent impact on the equilibrium conditions that determine price in food grain markets. When food supplies are cut back even a small amount, a large price rise is necessary to reduce demand. In the extreme case where consumers refuse to alter their food grain consumption when the price changes, ie, when h = 0, the model dictates that in long-term equilibrium fertiliser applications will not change either, no matter what happens to fe~ti~iser price. This is a startling conclusion for an economic model, but when consumer demand changes, the only mechanism in the model by which 150
FOOD POLICY
February
1976
Fertiliser andfood policy in LDCs
equilibrium can be maintained in the food grain market is through fertiliser-induced changes in supply. If consumer demand does not change when food grain prices have changed, then farmer demand for fertiliser will not change even though changed fertiliser prices set off the original food grain price change. The reason, of course, is that the food grain price must rise to whatever level is necessary to call forth the additional fertiliser applications. A supply shortage that reduces fertiliser demand in the short-term because of higher prices will have as its longer-term effect an increase in food grain prices sufficient to restore the profitability of the original level of fertiliser use.i5 Fuller specification of the model and consideration of its dynamic soften these conclusions in some circumstances and propertiesi provide cautionary provisos. But the model has direct relevance to the present food-fertiliser situation. The feed-back effect of low consumer price elasticities for basic foods to equilibrium prices for those foods and hence to relative profitability of applying fertiliser is too powerful to ignore. In effect, the model says that fertiliser applications must be profitable - because of the pressures for economic adjustment which imbalance between demand and supply of foodstuffs will create. If they are not, then relative prices must change until profitability returns. The algebra and logic of the model make it easy enough to understand analytically that food prices must rise relative to fertiliser prices so that food growing remains profitable. What is missing in the analysis is the political dilemma this poses in many developing countries. The next section discusses the policy ramifications of this model and of the empirical and analytical work that provided its foundations.
Fertiliser policy issues The political problem implicit in the macro-model stems from the functional relationship between the long-term equilibrium price of food grain and the price of fertiliser (and more generally, of all important agricultural inputs): b C
“If consumer response to higher food prices is nil, then positive response of fertiliser supplies to price is implied if this cobweb-type mechanism is ever to converge to a stable, long-term equilibrium. Otherwise, food production does not increase nor does food consumption decrease, resulting in a permanent disequilibrium. This can happen only in the model, not in the real world. ‘6 Both of these are discussed in Timmer, 1974. op cit. ” The impact of other parameter values is discussed in C. Peter Timmer, ‘Interaction of energy and food prices in less developed countries’, American Journal of Agricultural Economics, May 1975.
FOOD POLICY
February
1976
=f6
(A,, A,,
h, d) Pf b+h(b-l)
(21)
No necessary relationship exists between the price of food grain (P,) and the price of fertiliser (Ps) in the short-term because food grains and fertiliser clear on separate markets. But Equation(21) indicates a significant functional relationship between the prices of the two commodities in the long-term with the elasticity of response of PO to changes in Pf equal to a complex algebraic function involving b and h [b/b+h(b--I)]. If b = 0.14 and h = -0.2 as used in the model, this response elasticity has a value of 0.45. With these parameters, about half of any change in fertiliser price ultimately shows up in food grain prices.” This is the heart of the political dilemma. All poor alike, are sensitive to rising food prices. income growth add to the demand for food indirectly via demand for meat), the prices for begin to rise unless production is increasing at a
countries, rich and As population and grains (directly or these commodities rate about equal to 151
Fertiliser ardfood policy in LDCs
demand growth. One of the major sources of such productivity growth, especially in heavily populated, land-scarce countries is increased fertiliser use (especially in conjunction with modern varieties and improved environmental control). But with fertiliser scarce and two to three times more expensive than in the early 197Os, fertiliser-dependent gains in food grain production will be expensive gains in food grain production. Consequently, the ultimate policy goal of stabilising basic food prices via this traditional form of agricultural development is doomed to failure unless the resources are available to subsidise significantly the price of fertiliser. Few non-oil producing developing countries have this financial capacity. This particular scenario growing out of the macro-model is hard to complete except in pessimistic fashion. Only commercial farmers can reasonably protect their incomes in such a world, and even they must pay high real food costs. Subsistence farmers, still a substantial proportion of the poorer countries, may not be able to afford the higher food prices. They sell little food and benefit little from the higher price. Many subsistence farmers’ resource holdings are just not large enough to generate the real productivity necessary to be able to ‘afford’ to eat their own production. But the non-farm poor, urban and rural, are obviously the worst off because they must find a means to buy all their food. Cutting back on non-food expenditures offers little hope since many of these people spend nearly all their income on food already. Finding additional sources of income will be difficult by the very logic of the macromodel - food prices must rise relative to other prices, including the price of unskilled labour. The net result will be a reduction in food intake and slow starvation for those already at the nutritional margin. Despite the apparent logic of the model and the fact that the above scenario seems to be happening in a number of areas in the world, its conclusions are too grim to accept. There are other alternatives even within the context of the model that offer happier endings. In these alternatives the direct price relationships of the macro-model are circumvented by investments and policies designed to change the parameters in the intercept function U;;) in (2 1). Prices and incentives will still play an important, perhaps critical, role in this broader effort, but the effect will be through longer-term mechanisms. The investments and policies needed to shift the function cover a wide variety of fields and tcpics. In the longer-term attention must be focused on the elements in the consumption function intercept term (A,), and especially the population element in that term. It is difficult to imagine the populations of the developing countries still growing at the turn of the century at the same high rates as at the present. The slowing can be either Maithusian or enlightened. The former is the long-term result sketched above - the latter will require effort in basic rural good policy, good luck, and substantial development. Achieving basic rural development before population growth slows may involve revolutionary changes. But some important elements of rural development that have the twofold effect of providing employment in the countryside while raising agricultural productivity would seem to be broadly transferable. The construction of small feeder roads, secondary and tertiary irrigation canals, drainage ditches, terraces and the like can have a major impact on agricultural production, within a given price context. But to plan and carry out 152
FOOD POLICY
February
1976
Fertiliser audfood policy in L DCs
these projects successfully requires a judicious blend of local initiative and resource generation with national policies of support for local agricultural development that imply provision of skills and material not locally available and maintenance of agricultural incentives sufficient to provoke the local initiative. The character of these incentives is the natural focal point for the broad perspective of the macro-model. The important strategy dichotomy is over agricultural product price supports versus agricultural input price subsidies. Although the homogeneity condition that means a farmer reacts the same to a 10% increase in product price as to a 10% fali in fertiliser price is used in most analytical and empirical fertiliser demand work, it may not be true in developing countries.‘s Even if it is, most governments will not adopt a neutral position between raising food prices and subsidising fertiliser and other input prices as a means to stimulate agricultural production. Raj Krishna has well summarised the issues.19 The traditional argument prefers input subsidies because only farmers who use the modernising inputs wil! benefit and because food prices need not be increased by government policy. This strategy has been most popular in poor food-deficient countries with urban-based governments more concerned about food prices in the city than with rural welfare. Krishna suggests that product price supports might work better than input subsidies for a variety of reasons. Peasants tend to be more familiar with product prices and will probably be more sensitive to their variation. The critical factor for peasants is not insurance against high input prices but guarantees that product prices will not collapse leaving the cultivator helplessly in debt. Some input prices are difficult to subsidise - especially land and labour, which frequently form a large proportion of total costs - and only product price supports can be fully effective in stimulating output of particular crops. Input price subsidies do avoid immediate increases in food prices. But if the subsidy strategy fails to stimulate vigorous agricultural growth it will only lead to much higher food prices in the future as shortages materialise. ‘Thus if a support program does accelerate output growth it turns out to be a very profitable investment for the food consumers of a society.‘*’
Conclusions
” David’s estimates for a Laguna, Philippines sample significantly contradict the homogeneity condition, but Heady and Tweeten’s estimates for the Unites States seem to confirm it. Earl 0. Heady and Luther G. Tweeten, Resource Demand and Structure of the Agricultural Industry, Ames. Iowa, 1963. ” Raj Krishna, ‘Agricultural price policy and economic development’, in Agricultural Development and Economic Growth, H.M. Southworth and 6.J. Johnston feds), Ithaca, NY, 1967. 20 ibid, p 527.
FOOD POLICY
February
1976
Although we set off in search of non-price policy alternatives for increasing food production, at best we have been successful in pushing incentive prices to one stage removed. No doubt this is an unhappy prospect for those governments with a tradition of very low food prices intended to placate urban consumers. But in today’s world cheap food is not a policy option for countries without the agricultural productivity base to support it. Except for those countries with an abundance of natural resources relative to their population, this productivity base must be built through investment in agriculture. No government can make this investment entirely on its own account. Even in socialist countries most investment to raise agricultural productivity must be at the local level. Local incentives stimulated by national policies guaranteeing profitable prices provide a favourable environment in which local agricultural investment can take place.
Fertiliser atrdfoodpoliql in LDCs
A major portion of the research that forms the basis of this paper was supported by the Stanford Rice Project, funded under Contract No CM-ASIA-C-73-39 by the US Agency for International Development.
154
Though incentive prices play a critical role in establishing a favourable investment climate, it is important not to overstate their role in fostering agriculturai deveIopment. The analytical and empirical arguments developed here stress that little progress in boosting food production will be made in the absence of incentive prices. Clearly, little progress might also be achieved even with incentive prices if the other important facets of agricultural development are ignored or thwarted. These include development and locational adaptation of high yielding varieties, large and small scale water control works, development of modern input distribution markets to facilitate farmer access to fertiliser, pesticides, new seeds, and appropriate machinery, and markets to handle surplus grain production resulting from the development efforts. Small farmers will need special credit facilities to enable them to participate on a competitive basis with larger and better financed farmers. For both producer and consumer protection some type of seasonal buffer stock programme will be necessary to eliminate the extreme variations in seasonal prices that are characteristic of food grain economies with a small marketed surplus, poorly developed market information, and a private grain trade inadequately financed and experienced to handle the increasing share of grain moving into urban markets. Most governments will be tempted to and probably should play an active role in this price buffering effort during the early transition from backward to modern agriculture. But experience so far casts strong doubts on any central government’s ability to handle efhciently a large proportion of the basic food grain needs of a society approaching modernity. Policies designed to encourage a vigorous and competitive private grain trade will probably be more efficient in the long-term. Whether the problems are larger than governments’ abilities to deal with them is a haunting theme throughout this paper. Analysis and history are not entirely reassuring. At best they suggest which paths are dead-ends and which, in particular circumstances, have led to success. The transferability of the paths without also transferring all or most of the circumstances is unknown. This removes food and fertiliser policymaking from the realm of analysis and places it in the realm of judgment, experience, and politics.
FOOD POLICY
February
1976
C. Peter Timmer
At
a time
high
of fertiliser
prices
more
about
the
relationships food
are
prices
for
dilemma ting
to
the these and
Impact attention
of
Peter
Professor of
University,
poses
food
of a simple
price
a
attempproduction Through
are
discussed they
receives of
its
special frequen;
Timmer
is
of
Economics,
Nutritional Ithaca,
H.E.
Sciences, New
York,
Babcock DiviCornell 14850,
USA.
’ Christina Crisostomo David, A Model of Fertilizer Demand of Asian Rice Farms: A Micro-Mac0 Analysis, Unpublished PhD Dissertation, Stanford University.
1975. ‘The countries were Japan, South Korea, Taiwan, (West) Malaysia, Sri Lanka, Indonesia, Thailand, Philippines, Burma, India, and Pakistan-Bangladesh. The addition of India and Pakistan-Bangladesh plus a much longer time period enabled David to achieve better results than Timmer and Falcon reported for a similar model. See C. Peter Timmer and Walter P. Falcon, ‘The political economy of rice production and trade in Asia,’ in Agriculture in Development Theory, Lloyd Reynolds led), Yale University Press, 1975 and ‘The impact of price on rice trade in Asia’, in Agriculture, Trade and Development George Tolley, (ed). Ballinger Press, 1975.
FOOD POLICY
February
Fertiliser to food
offer.
instrument.
Food
Fertiliser usage is a critical determinant of food production and an understanding of the factors affecting the level of fertiliser application on food crops is essential to an understanding of the global food situation. It is no simple task because interrelationships among fertiliser use, food supplies, and prices are complex and multidirectional. Food and fertiliser policies have frequently ignored all but the most obvious of these relationships and consequently have often been counterproductive in the long and sometimes even in the shortterm.
macro-model
insights
because
use as a policy
sion
which
rising prices.
policy
in-
in increased
relationships the
and
price
governments
increase
use
understood
Fertiliser
food
for
and
to know
fertiliser
reflected
and minimise
C.
poorly
between
production.
creases
scarcity
it is important
1976
Four separate categories of fertiliser to food conversion rates must be considered: short-term versus long-term and micro-functions versus macro-functions. Short-term functions can be either micro or macro and refer to food grain yield increments achieved by fertiliser increments holding constant all or most other factors of production. In particular, short-term responses assume no changes in plant varieties, water control, and general cultural practices. Long-term responses refer to the food grain yield increment achieved by fertiliser increments when all or most other factors of production have also adapted to the new fertiliser levels. Naturally, long-term responses are expected to be significantly larger than short-term responses. The distinction between micro- and macro-functions refers to the level of the response observation. Small plot trials for example, in farmers’ fields or in experimental stations, yield the data used to calculate micro-functions. Aggregate data on yields and fertiliser use by regions or even countries are used to calculate macro-response functions. Clearly, a continuity exists between micro and macro and between short-term and long-term, but the distinctions are both conceptually and empirically useful. Current work by Christina Crisostomo David for the Stanford Rice Project’ not only illustrates the distinctions nicely but also provides extremely important quantitative parameters that are an essential part of both the analytical and policy discussions that follow. The results hold specifically for the Asian rice economy (excluding China), but the issues are general. David examined data for rice production, area harvested, and fertiliser consumption for 11 countries* for the years 1950 to 1972. A 143
Fertiliser arldfood policy it1L DCs
simple Cobb-Douglas
(log linear) production
function of the form
R = AiHT’, where
R = Rice production H = Area harvested, F = Fertiliser nutrients applied, and Ai, a, b = Estimated parameters,3 showed the following results: R = 1.09 Ho.859 F0-143 (R2 = 0.946) R = A,I HI.444
3Ai is the intercept term for the function, where i refers to the country-specific intercept. Alternatively a single intercept for all countries can also be estimated as in (1). Parameters a and b are the production elasticities ot area and fertiliser respectively. 4This result is nearly identical to the Timmer-Falcon findings although the absolute magnitudes here are significantly lower, probably reflecting the longer time period in the sample before fertiliser responsive varieties were widely available. Further discussion of the logic of this model and more detailed results are contained in the works cited in footnotes 1 and 2. 5The six countries were the Philippines, India. Pakistan, Indonesia. West Malaysia, and 7.hailand. See David, op cit. for details of village locations, environment and so on.
144
F0.073
(1)
(RZ = 0.99 1)
(2)
Both (1) and (2) are macro-functions because they are estimated from national aggregate data. Equation (1) can be interpreted as a long-term function, however, and equation (2) as a short-term function. The difference is that in (1) a single function is estimated for all eleven countries in the sample - the assumption being that in the long-term all countries can invest in water control projects, suitable plant varieties, and cultural practices to enable the countries with below average yields to achieve the yields reached by the above average countries. In this long-term environment the response elasticity of rice production to higher fertiliser levels is O-143, ie, a 10% increase in fertiliser application would increase rice production by 1.43% with the same area harvested. Equation (2) permits each country in the sample to have its own intercept term (not shown except as Ai). The production elasticities are still constrained to be the same for all countries, but the starting point (yield with zero fertiliser) is unique to each country. This permits the fertiliser response to relate to each country’s specific environment, and thus the estimated parameter can be interpreted as a short-term response. Equation (2) shows that this short-term response is about half that of the long-term response.4 In short, yield response to fertiliser in the long term after environmental, varietal, and cultural changes are also forthcoming is about twice as large as the response in the short term. The variation between micro- and macro-functions is equally dramatic. Because of the relative continuity in aggregation from field plots all the way to supra-national (eg, ‘Asian’) boundaries, the distinction can be illustrated at a number of levels. For example, in the macro model shown in (1) and (2) country-specific fertiliser response elasticities can be estimated as well as country-specific intercepts. These range from 0.02 in Burma to O-43 in Sri Lanka and Taiwan while the mean for the sample with separate intercepts, shown in (2) was 0.07. Obviously very substantial differences exist, even at national aggregate levels, between physical response rates to changing fertiliser applications. Not surprisingly, the differences become even more dramatic at lower levels. David was able to estimate the same production function model shown in (1) and (2) for a set of Asian farm level data from 33 villages in six countries for the wet season of 197 1/72.5 The results are shown in Equations (3) and (4). R = 1.2 HO-813 ~0.124 (R2 = 0.754) (3) R =A ,Jf0'837 F0.095 (R2 = 0.862) I (4)
FOOD POLICY
February
1976
Fertiliser
’
The data are drawn from FAO, ‘Statistics of crop responses to fertilisers’. Freedom from Hunger Campaign Fertilizer Program, Rome, various years.
Country
FOOD POLICY
February
1976
policy in L DCs
Once again, (3) and (4) show the expected decrease in fertiliser response, from O-124 to O-095, from long-term to short-term. The decrease is not so dramatic as before because the sample refers specifically to the wet season crop only. The villages studied were selected on the basis of good water control, and modern varieties were extensively used (except in Thailand). Consequently the potential difference in environmental varieties and cultural practices is not as wide as in the national data set, and so the short-term and long-term fertiliser responses are closer. When separate fertiliser response elasticities are estimated, they range from 0.016 in Sidomulyo, East Java, Indonesia, to 0.386 in Mahipon, Geipan, Nueva Ecija, the Philippines. Within the Philippines, for example, the range is 0.05 to 0.386. A further illustration of the wide variation in fertiliser response to be expected from site to site is given in Table 1. These data6 still refer only to rice, and yet the nitrogen response alone varies from 4.7 kg of rice per kilogram of nitrogen in Burma to 10.5 kg in Bangladesh. Statistics for other crops show wider variations because the environments tend to vary more than the Asian rice environment. Where does this leave us in undersatnding the response of food grain to fertiliser? For rice, FAO has reported a relationship on a worldwide basis between increases in yield of paddy and applied nitrogen.’ On the basis of data from 385 samples in 20 countries, a linear response function showed a 12-13 kg increase in paddy production for every kilogram increase in nitrogen. This high response rate should be considered as a long-term potential due to the pooled nature of the data. It offers little direct aid to a policy-maker concerned about short-term response in a particular environment. For this, attention must be turned to the type of results produced by David, though these must be used with caution because of the variation she observes from village to village and from year to year (in a third sample not discussed here). A substantial analytical understanding, however, has been achieved at the macro-level about differences between short-term and longterm responses. Since a developing nation’s food policy must appropriately balance polices to achieve short-term needs and those designed for longer-term goals, this understanding is critical to the subsequent discussion of food and fertiliser policy alternatives.
Table 1. Rice yield responses from fertiliser
a Computed from the report, Statistics of Crop Responses from Fertilizer. Food and Agriculture Organization, Rome 1966. Increases in yields are those resulting from application of 30 kg of each plant nutrient per hectare except in the case of South Korea where yield increases are those resulting from 60 kg.
andfood
Burma Sri Lanka Bangladesh Ghana India Iran Thailand Vtet-Nam South Korea
Yield per hectare without fertiliser
applications,
selected countriesa
Increase in yield per kilogram
Nitrogen
Phosphate
Kg
Kg
Kg
1432 1476 991 749 1230 2049 1172 1271 2350
4.7 5.2 1IO.5 9.1 9.9 7.6 9.0 5.4 8.0
2.6 5.4 7.1 9.1 6.5 8.2 8.5 3.0 0.5
of
Potash Kg 1 .o 1 .o 3.3 6.1 _ 1 .2 3.2 0.7 2.3
145
Fertiliser andfood policy in LDCs
Farmers’ use of fertiliser The factors affecting any farmer’s fertiliser use can be conveniently grouped into three broad categories: 1. environmental factors, especially the physical response of the crop to fertiliser, 2. economic factors, especially the price of fertiliser relative to the price for which the crop can be sold but also including any capital or credit constraints on how much fertiliser can be purchased, and 3. the conditions of knowledge about fertiliser, the degree of uncertainty surrounding the results of its use, and the attitude about attendant risks.8
’ FAO. The Response of Rice to Fertilizer. Rome, 1966. ’ Each of these categories is a major field of research and published findings. The interested reader will find further discussion and bibliographic references in the works cited in other footnotes. ’ Zvi Griliches. ‘The demand for fertilizer: an economic interpretation of a technical change’, Journal of Farm Economics, August 1958 and ‘Distributed lags, disaggregation, and regional demand functions for fertilizer’, Journal of Farm Economics, February 1959. ” Griliches calculated that about 80% of the adjustment takes place in five years.
GRILICHES’
MODEL
Let barred the relevant
letters denote logarithms variables. Then
?; = c where
F;
+
of
d Pft + et,
= desired fertiliser consumption in long-term equilibrium,
Pfr = price of fertiliser at time t relative to the price of agricultural output at time t, and
e*
146
= a random term.
disturbance
The primary concern here is to delineate those factors susceptible to influence by government policy and to sense the quantitative significance and flexibility of each. The issues can best be understood in the context of what has become the standard model used to explain the dynamics of fertiliser consumption, the lagged adjustment model first used for this purpose by Zvi Griliches.’ (see below) Griliches’ result for US demand for total fertiliser nutrients over the 19 11-1956 period indicated a highly significant, short-term elasticity of about -0.5 and an adjustment coefficient of about O-25. Taken together, these two coefficients imply a long-term elasticity of about -2.0. That is, if fertiliser prices increase by 10% (with crop prices constant), then fertiliser use drops by about 5% in the first year and by about 20% after the price increase has been maintained for a number of years.‘O Because the model requires a time series of observations on fertiliser use and price, it has been applied to only a few developing countries. Table 2 summarises those studies that have come to light so far. Although the elasticity and adjustment coefficients do not seem consistent at first glance, a number of the more extreme values are explained by fairly obvious statistical problems in data and specification. With this proviso, a fair summary would be that in developing countries for which studies have been done, the short-term price elasticity of demand for fertiliser is about -0.5 to -1.0, and the long-term elasticity, assuming the new relative prices are maintained,
The relative prices of other important inputs should also enter Equation (5). but they were not significant in Griliches’ work and are omitted for simplicity. The use of relative price implies an equal farmer adjustment to a one percent rise in output price or to a one percent fall in fertiliser price. The validity of this assumption seems to vary from developed to less developed agricultures, with enormous implications for government pricing policies. The issue is re-examined in the last section. The amount of fertiliser actually used at time t is equal to the desired (or appropriate) amount only in long-term equilibrium. The adjustment of actual towards desired use is through proportional changes, as in Equation (6). Fr - Ft-, = r (et* - Ft-lI, (6)
where
Ft
r
= actual fertiliser consumption in time t. and = the adjustment coefficient (O
The model is completed ey substituting (5) into (6) and solving for Ft, as follows: ~t=~+drPft+(l-rlFt_,+ret.
(7)
With appropriate statistical precautions taken with regard to autocorrelation of the residuals, Equation (7) is suitable for direct estimation. Since the variables are in logarithms, the short-term elasticity of demand for fertiliser with respect to its relative price is given by the estimated and the long-term coefficient dr, elasticity, after all adjustments have been made, is given by d.
FOOD POLICY
February
1976
Fertiliser and food policy in L DCs Table 2. Summary
of fertiliser
demand studies in developing Elasticity
Country
Time period
Short-term
Brazil
1949-71
-1.12b
India
1953/4-67/B 195819-6314
937
of demanda Long-term
Comments OLS estimate with area cultivated included in equation. Real average price of all fertiliser, significant autocorrelation. Same as traditronal model but no autocorrelation using dynamic model.
-o.33c
-1.94
-0.31d -0,530 -I .2d
-0.34 -6.63 -2.5b
_
-0.74b
Price elasticity estrmated by OLS for total fertiliser use utilising five years averages as observations. Thus the elasticity is more long-term than short-term in nature.
-0.88
Lagged adjustment total fertrliser paid OLS estimate from Equation contains
1883-l
Korea
1960-7 1
-0.17
1971
-0.7Ob
_
Philippines
1958-72
0.59b
_
Taiwan
1950-66
-o.55c
_
-2.03b
a Demand elasticity for all nutrients unless otherwise noted. b Denotes significance at 0.9 or higher. c Denotes significance between 0.8 and 0.9. d Denotes significance between 0.7 and 0.8. Source: C. Peter Timmer. ‘The demand for fertilizer in developing countries’, Food Research institute Studies. Vol XIII. No 3. 1974.
” A much fuller treatment of these results is contained in C. Peter Timmer, ‘The demand for fertilizer in developing countries’, Food Research Institute Studies, Vol XIII, No 3, 1974. “Wallace Huffman,‘Decision-making:the role of education’, American Journal of AgriculturalEconomics, February 1974. l3 Timmer, 1974, op cit.
February
Adjustment coefficient
_
Japan
FOOD POLICY
countries
1976
-2.99
0.92 0.08 0.50
_
0.68
Demand function for nitrogen fertiliser contains area irrigated Same, but equation excludes area irrigated. Analysis of covariance results for nrtrogen consumption using Indian state data. The short-term response is from an equation that includes separate state intercepts; the long-term elasticity excludes them. The adjustment coefficient is calculated from the two responses.
model using deflated price index of by farm. cross-section survey of 300 farmers. many other farm specific variables.
OLS: Equation includes sugar and corn hectarage and rice yield. Price elasticity for CIF nitrogen value deflated by consumer price index. Demand function for nitrogen using relative price of rice to nitrogen. Equation contains lagged yield which is highly significant. Dynamic equation excludes lagged yields but price and fertiliser variables the same.
is in the range of -1.5 to about -3-O. Naturally these results must be used with great caution, but they do show a widespread tendency for farmers in developing countries to behave ‘appropriately’ with respect to fertiliser prices.” In Griliches’ simple dynamic adjustment model the adjustment coefficient (r) serves to capture the impact of all non-price variables that have an impact on the level of fertiliser use. Thus, although capital availability, knowledge, and (flexible) environmental factors may be limiting in the short-term, their impact in this model is through the rate of adjustment and not through the level of long-term demand. A functional specification of r would be a major improvement in this model because it would permit separate quantitative estimates of the impact of these other factors that are presently swept into a single effect. W. Huffman has constructed a model for this purpose to test the effect of education on the rate at which farmers adjust fertiliser use on corn due to changes in fertiliser prices.” Extension of his model to include other obvious variables income or assets, length of experience, extent of knowledge, types of crops, etc - would be a major contribution. For developing countries with limited experience in the use of fertiliser some of these variables should also appear directly in the long-term function. As I showed in an earlier article a dynamic adjustment model is still entirely appropriate in this context.13 In the second stage of her analysis, David tackles these types of questions directly. Her goal is to measure the separate impact of price and environmental variables on the demand for fertiliser. To do so, she uses the country (or village) specific intercept and fertiliser 147
Fertiliser audfood
poliq,
in idDCs
elasticities estimated in the production functions discussed earlier as semi-continuous variables along with relative fertiliser price in a loglinear demand function. Despite the apparent obviousness of this approach14 David appears to be the first to use it successfully. Her results are important both for the actual magnitudes and their amazing uniformity between macro- and micro-functions. They are summarised in Table 3. The price elasticities shown in Table 3 do not correspond directly to the long- and short-term elasticities shown in Table 2 because David’s estimates are based on a simple log-linear model rather than the dynamic adjustment model (because the data are mostly cross-sectional). Still, there are short-term and long-term implications in these results that are important.
” For instance, the fertiliser demand function derived from the profit-maximising conditions applied to a Cobb-Douglas production function contains just these terms.
Equations (7) and (10) give the simplest possible fertiliser demand function with no attempt to hold environment or varieties constant in the analysis. The result is a price elasticity of about -0.9 for both the macro and micro data. Assuming that all environmental and varietal adaptions had already been made to take advantage of optional fertiliser use then the two estimates provide a reasonable longer-term price elasticity of demand. However, since we are probably dealing with situations where this perfect adaption has not yet occurred and when there will be lags in achieving it, the model underestimates the true long-term elasticity which would apply assuming there had been perfect adaption in all respects. As increasing attention is paid to environmental differences, and then to varietal difference, ie, in (8), (9) and (1 I), (12), the estimated price elasticity drops significantly. In the macro-function, where environmental and varietal differences among countries are quite substantial, the elasticity drops to -0.5 when production function intercepts and output elasticities are entered and to -0.3 when differences in use of modern varieties are considered. Thus the shortterm price response of Asian farmers to fertiliser price changes is about -0.5 when environment is not permitted to change and about -0.3 when neither environment nor varities can change. As expected, the micro-function shows less dramatic changes in price elasticity with added environmental variables because the environment does not differ nearly so much as in the macro-sample Table 3. Logarithmic
fertiliser
Equation no.
Fertiliserrice price
demand functions Production
a These data are from the set of 188 observations used to estimate Equations (I) and (2). The fertiliser data were roughly adjusted for amounts used on non-rice crops. b These data are from the set of over 2400 observations used to estimate Equations (31 and (4). ‘Modern varieties’ refers to the proportion of area planted to high-yielding modern varieties. Note:
Figures in parentheses
are f-values.
Source: Cristina Crisostoma David, A Model of Fertiiker Demand of Asian Rice Farms: A Micro-Macro Analysis, Unpublished PhD dissertation, Stanford University, 1975.
Intercept
“Adjusted” (7)
1.874
(8)
1.639
(91
0,945
-0~922 t-4.056) -0.510 (-3-472) -0.320 1-2-989)
Intercept
2.035
(11)
1.562
112)
1.527
-0.863 (7.874) -0.691 b-6.245) -0.650 (-5.529)
Modern varieties
R‘J
Aggregate Asian Data, 1950.1972a
Data for 33 Selected (IO)
function output elasticity
-
-
0.084
0.855
7.334
-
0.639
(3.606) 0.267 (1.506)
(7,677) 3.705 (4.959)
_
0.864 112.7481
0.812
Asian Villages, Wet Season Ig70/7Ib _ 0,584 (7-368) 0.580 (7.307)
0.170 2.326 68.7393 2.336 (8.768)
-
0.038 (0.037)
FOOD POLICY
0.252 0.253
February
1976
Fertiliser andfood polic)! it1L DCs
the villages were chosen for their relative homogeneity in water control and so on. Thus the coefficient attached to the output elasticity is uniformly smaller (but not’less significant due to the larger sample) than in the macro-sample. Further, the ‘modern varieties’ coefficient is not at all significant in the micro-function, reflecting the lack of variation. The ultimate result is a fairly high confidence in a short-term price elasticity at the micro-level of about -0.6. Which is more important in determining fertiliser demand - price or environment? On the basis of her entire analysis, the depth of which has only been hinted at here, David judges that roughly onethird of the explained variation in fertiliser use is accounted for by price differences and the remaining two-thirds by the environmental and varietal factors. This leaves unanswered the determinants of the flexibility of these environmental variables and the costs of changing them. David also points out that changes in environment and varieties may be price-incentive related, as indicated above. One last empirical issue is also left dangling. The models and results so far have assumed an independence between fertiliser prices and output prices, an independence which seems natural in view of economists’ normal models of short-term farm decision-making. But the desirability of having both short-term and long-term macroparameters for policy planning at national and international levels calls into question this mode1 and the results obtained from it.
Long-term fertiliser demand In the short term, farmers assume that the prices of their inputs and expected prices of their output are fixed and not subject to influence by individual decisions about input and output combinations and levels. But in the long term, prices in food grain markets depend on food grain supplies available relative to demand. The level of these supplies from domestic and international sources depends at least partially on the use of fertiliser in the production of the commodity. The interrelationships between food demand functions and production functions that determine the longer-term equilibrium at the macro-market level also in turn have significant implications for the long-term response of farmers to change in the price of fertiliser and then for the level of food prices relative to fertiliser prices. A simple long-term macro-mode1 for fertiliser demand (see over) illustrates these points. The mode1 shows that striking differences exist between short-term response elasticities to fertiliser price changes and long-term elasticities. Using David’s value of 0.14 for the response elasticity of food grain production in the model the short-term fertiliser demand elasticity is -1.16, and the food grain supply elasticity is -0.16. The comparable long-term elasticities are -0.64 and -0.09 respectively. These long term values are only about halfthe short-term values, a result just the reverse of what was found for short-term versus longterm micro-functions (see Table 2). The source of the paradox lies in the aggregation from micro to macro and is a familiar issue to macro-economists. The driving mechanism of the fertiliser demand macro-mode1 is the link between food grain prices and supplies forthcoming from the production function. This link is entirely missing in a micro-context. In the simplest version of the mode1 used here food grain supplies depend FOOD POLICY
February
1976
Fertiliser audfoodpoliq LONG-TERM This section
DEMAND summarises
in LDCs b,h a
originally presented in a fertiliser demand context (see footnote 1 1 above) and later extended to more general food-energy relationships.* The interested reader is referred to these two articles for details of the models and fuller discussion of varieties and implications. The model is built from an aggregate production function of the sort estimated empirically in Equation (I) and an aggregate consumption function for one of the major food grains. Since our primary interest is in fertiliser and the food grain price, the model subsumes other important variables into the intercept terms.
When farmers treat the price of grain, PO, and the price of fertliser, 4, as given in the short-term, the assumption of profit maximisation applied to the production function yields a short-term demand function for fertiliser and consequently a short-term supply function for food grains.
FX sr al’s,sr
Q,
=A,Fb
= Ff fA,,b)
= f21As.b)
where
Qs,Qd
= AdPoh
Pf
Pf
f b-l
___b b-l
Po
Po
I l-b
(151
- b l-b
(16)
(13) where
ad
.T response elasticities of food grain production and consumption respectively.
model
FTr
(14)
= quantity of food grains produced and consumed in a given year,
F
= fertiliser
appiications
As
= an intercept term that captures the effect of other important production inputs such as water, labour, etc
Q$,.
fi f‘A,,b)
= the short-term equilibrium input of fertiliser food into grain production, assuming profit maximisation, short-term -the equilibrium level of food grain supplies, and = functions of coefficients in the production function
PO = the price of food grain Ad
= an intercept term that captures the effect of other important demand factors such as population inand come, and
The equilibrium price of food grain in this model, PO”,can be found by equating the demand and supply functions and solving.
AdPoh P,
* C. Peter Timmer. ‘Interaction of energy and food prices in less developed countries’, American Journal of Agricultural Economics, May 1975.
(171
= AsFb = f$A,Ad
$-
(16)
The solution value is used in Equations 15 and 16 to find the long-term equilibrium values of fertiliser use and food grain supplies as a function of fertiliser price.
5: =fir’As,
Ad, h, bf P+‘--i7
(19)
hb as*,,. = fs (As, Ad, h, 6) Pfb+h’b-‘)
I
where
F* ii
0”
s,ir
(213)
= the long-term equilibrium level of fertiliser use in food grain productions, and
= the
long-term equilibrium level of food grain supplies.
Striking differences exist between the short-term elasticities to response fertiliser price changes in Equations (15) and (16) and the long-term elasticities in Equations (19) and (20). If b = 0.14, the value in David’s long-term macro-function shown in Equation( 11, then the short-term fertiliser
demand
elasticity
(,A,)
is -1.16,
and the food grain supply elasticity
(,A+
is -0.16. Happily, this fertiliser demand elasticity calculated from David’s longerterm physical response elasticity of 0.14 coincides fairly well with her own estimated demand elasticity of about -0.9 in her macro function (Table 3). The time perspective of the two models is different so that ‘long-term’ in her functions refers to changes in variety and environment but does not correspond to long-term market equilibrium in the macro-model under discussion here. The comparable long-term equilibrium responses of fertiliser use and food grain supplies to changes in fertiliser
pricesarefbw, h
)and (hbIThat b+hlb- 1) is, the degree of price response from the food grain consumption fbnction, h, is an important parameter in determining the elasticities to long-term response changes in fertiliser prices. If b = 0.14 as before, and h = -0.2. a reasonable value for a basic food grain with few subthe stitutes, respective long-term elasticities are -0.64 and -0.09.
entirely on the level of fertiliser application. (Other factors are held constant in the intercept term.) When the price of fertiliser rises exogenously, as when the raw feed stocks for fertiliser plants become scarce or shipping costs increase, the short-term effect is to cut back fertiliser applications according to the short-term elasticity (&). But this has a subsequent impact on the equilibrium conditions that determine price in food grain markets. When food supplies are cut back even a small amount, a large price rise is necessary to reduce demand. In the extreme case where consumers refuse to alter their food grain consumption when the price changes, ie, when h = 0, the model dictates that in long-term equilibrium fertiliser applications will not change either, no matter what happens to fe~ti~iser price. This is a startling conclusion for an economic model, but when consumer demand changes, the only mechanism in the model by which 150
FOOD POLICY
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1976
Fertiliser andfood policy in LDCs
equilibrium can be maintained in the food grain market is through fertiliser-induced changes in supply. If consumer demand does not change when food grain prices have changed, then farmer demand for fertiliser will not change even though changed fertiliser prices set off the original food grain price change. The reason, of course, is that the food grain price must rise to whatever level is necessary to call forth the additional fertiliser applications. A supply shortage that reduces fertiliser demand in the short-term because of higher prices will have as its longer-term effect an increase in food grain prices sufficient to restore the profitability of the original level of fertiliser use.i5 Fuller specification of the model and consideration of its dynamic soften these conclusions in some circumstances and propertiesi provide cautionary provisos. But the model has direct relevance to the present food-fertiliser situation. The feed-back effect of low consumer price elasticities for basic foods to equilibrium prices for those foods and hence to relative profitability of applying fertiliser is too powerful to ignore. In effect, the model says that fertiliser applications must be profitable - because of the pressures for economic adjustment which imbalance between demand and supply of foodstuffs will create. If they are not, then relative prices must change until profitability returns. The algebra and logic of the model make it easy enough to understand analytically that food prices must rise relative to fertiliser prices so that food growing remains profitable. What is missing in the analysis is the political dilemma this poses in many developing countries. The next section discusses the policy ramifications of this model and of the empirical and analytical work that provided its foundations.
Fertiliser policy issues The political problem implicit in the macro-model stems from the functional relationship between the long-term equilibrium price of food grain and the price of fertiliser (and more generally, of all important agricultural inputs): b C
“If consumer response to higher food prices is nil, then positive response of fertiliser supplies to price is implied if this cobweb-type mechanism is ever to converge to a stable, long-term equilibrium. Otherwise, food production does not increase nor does food consumption decrease, resulting in a permanent disequilibrium. This can happen only in the model, not in the real world. ‘6 Both of these are discussed in Timmer, 1974. op cit. ” The impact of other parameter values is discussed in C. Peter Timmer, ‘Interaction of energy and food prices in less developed countries’, American Journal of Agricultural Economics, May 1975.
FOOD POLICY
February
1976
=f6
(A,, A,,
h, d) Pf b+h(b-l)
(21)
No necessary relationship exists between the price of food grain (P,) and the price of fertiliser (Ps) in the short-term because food grains and fertiliser clear on separate markets. But Equation(21) indicates a significant functional relationship between the prices of the two commodities in the long-term with the elasticity of response of PO to changes in Pf equal to a complex algebraic function involving b and h [b/b+h(b--I)]. If b = 0.14 and h = -0.2 as used in the model, this response elasticity has a value of 0.45. With these parameters, about half of any change in fertiliser price ultimately shows up in food grain prices.” This is the heart of the political dilemma. All poor alike, are sensitive to rising food prices. income growth add to the demand for food indirectly via demand for meat), the prices for begin to rise unless production is increasing at a
countries, rich and As population and grains (directly or these commodities rate about equal to 151
Fertiliser ardfood policy in LDCs
demand growth. One of the major sources of such productivity growth, especially in heavily populated, land-scarce countries is increased fertiliser use (especially in conjunction with modern varieties and improved environmental control). But with fertiliser scarce and two to three times more expensive than in the early 197Os, fertiliser-dependent gains in food grain production will be expensive gains in food grain production. Consequently, the ultimate policy goal of stabilising basic food prices via this traditional form of agricultural development is doomed to failure unless the resources are available to subsidise significantly the price of fertiliser. Few non-oil producing developing countries have this financial capacity. This particular scenario growing out of the macro-model is hard to complete except in pessimistic fashion. Only commercial farmers can reasonably protect their incomes in such a world, and even they must pay high real food costs. Subsistence farmers, still a substantial proportion of the poorer countries, may not be able to afford the higher food prices. They sell little food and benefit little from the higher price. Many subsistence farmers’ resource holdings are just not large enough to generate the real productivity necessary to be able to ‘afford’ to eat their own production. But the non-farm poor, urban and rural, are obviously the worst off because they must find a means to buy all their food. Cutting back on non-food expenditures offers little hope since many of these people spend nearly all their income on food already. Finding additional sources of income will be difficult by the very logic of the macromodel - food prices must rise relative to other prices, including the price of unskilled labour. The net result will be a reduction in food intake and slow starvation for those already at the nutritional margin. Despite the apparent logic of the model and the fact that the above scenario seems to be happening in a number of areas in the world, its conclusions are too grim to accept. There are other alternatives even within the context of the model that offer happier endings. In these alternatives the direct price relationships of the macro-model are circumvented by investments and policies designed to change the parameters in the intercept function U;;) in (2 1). Prices and incentives will still play an important, perhaps critical, role in this broader effort, but the effect will be through longer-term mechanisms. The investments and policies needed to shift the function cover a wide variety of fields and tcpics. In the longer-term attention must be focused on the elements in the consumption function intercept term (A,), and especially the population element in that term. It is difficult to imagine the populations of the developing countries still growing at the turn of the century at the same high rates as at the present. The slowing can be either Maithusian or enlightened. The former is the long-term result sketched above - the latter will require effort in basic rural good policy, good luck, and substantial development. Achieving basic rural development before population growth slows may involve revolutionary changes. But some important elements of rural development that have the twofold effect of providing employment in the countryside while raising agricultural productivity would seem to be broadly transferable. The construction of small feeder roads, secondary and tertiary irrigation canals, drainage ditches, terraces and the like can have a major impact on agricultural production, within a given price context. But to plan and carry out 152
FOOD POLICY
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1976
Fertiliser audfood policy in L DCs
these projects successfully requires a judicious blend of local initiative and resource generation with national policies of support for local agricultural development that imply provision of skills and material not locally available and maintenance of agricultural incentives sufficient to provoke the local initiative. The character of these incentives is the natural focal point for the broad perspective of the macro-model. The important strategy dichotomy is over agricultural product price supports versus agricultural input price subsidies. Although the homogeneity condition that means a farmer reacts the same to a 10% increase in product price as to a 10% fali in fertiliser price is used in most analytical and empirical fertiliser demand work, it may not be true in developing countries.‘s Even if it is, most governments will not adopt a neutral position between raising food prices and subsidising fertiliser and other input prices as a means to stimulate agricultural production. Raj Krishna has well summarised the issues.19 The traditional argument prefers input subsidies because only farmers who use the modernising inputs wil! benefit and because food prices need not be increased by government policy. This strategy has been most popular in poor food-deficient countries with urban-based governments more concerned about food prices in the city than with rural welfare. Krishna suggests that product price supports might work better than input subsidies for a variety of reasons. Peasants tend to be more familiar with product prices and will probably be more sensitive to their variation. The critical factor for peasants is not insurance against high input prices but guarantees that product prices will not collapse leaving the cultivator helplessly in debt. Some input prices are difficult to subsidise - especially land and labour, which frequently form a large proportion of total costs - and only product price supports can be fully effective in stimulating output of particular crops. Input price subsidies do avoid immediate increases in food prices. But if the subsidy strategy fails to stimulate vigorous agricultural growth it will only lead to much higher food prices in the future as shortages materialise. ‘Thus if a support program does accelerate output growth it turns out to be a very profitable investment for the food consumers of a society.‘*’
Conclusions
” David’s estimates for a Laguna, Philippines sample significantly contradict the homogeneity condition, but Heady and Tweeten’s estimates for the Unites States seem to confirm it. Earl 0. Heady and Luther G. Tweeten, Resource Demand and Structure of the Agricultural Industry, Ames. Iowa, 1963. ” Raj Krishna, ‘Agricultural price policy and economic development’, in Agricultural Development and Economic Growth, H.M. Southworth and 6.J. Johnston feds), Ithaca, NY, 1967. 20 ibid, p 527.
FOOD POLICY
February
1976
Although we set off in search of non-price policy alternatives for increasing food production, at best we have been successful in pushing incentive prices to one stage removed. No doubt this is an unhappy prospect for those governments with a tradition of very low food prices intended to placate urban consumers. But in today’s world cheap food is not a policy option for countries without the agricultural productivity base to support it. Except for those countries with an abundance of natural resources relative to their population, this productivity base must be built through investment in agriculture. No government can make this investment entirely on its own account. Even in socialist countries most investment to raise agricultural productivity must be at the local level. Local incentives stimulated by national policies guaranteeing profitable prices provide a favourable environment in which local agricultural investment can take place.
Fertiliser atrdfoodpoliql in LDCs
A major portion of the research that forms the basis of this paper was supported by the Stanford Rice Project, funded under Contract No CM-ASIA-C-73-39 by the US Agency for International Development.
154
Though incentive prices play a critical role in establishing a favourable investment climate, it is important not to overstate their role in fostering agriculturai deveIopment. The analytical and empirical arguments developed here stress that little progress in boosting food production will be made in the absence of incentive prices. Clearly, little progress might also be achieved even with incentive prices if the other important facets of agricultural development are ignored or thwarted. These include development and locational adaptation of high yielding varieties, large and small scale water control works, development of modern input distribution markets to facilitate farmer access to fertiliser, pesticides, new seeds, and appropriate machinery, and markets to handle surplus grain production resulting from the development efforts. Small farmers will need special credit facilities to enable them to participate on a competitive basis with larger and better financed farmers. For both producer and consumer protection some type of seasonal buffer stock programme will be necessary to eliminate the extreme variations in seasonal prices that are characteristic of food grain economies with a small marketed surplus, poorly developed market information, and a private grain trade inadequately financed and experienced to handle the increasing share of grain moving into urban markets. Most governments will be tempted to and probably should play an active role in this price buffering effort during the early transition from backward to modern agriculture. But experience so far casts strong doubts on any central government’s ability to handle efhciently a large proportion of the basic food grain needs of a society approaching modernity. Policies designed to encourage a vigorous and competitive private grain trade will probably be more efficient in the long-term. Whether the problems are larger than governments’ abilities to deal with them is a haunting theme throughout this paper. Analysis and history are not entirely reassuring. At best they suggest which paths are dead-ends and which, in particular circumstances, have led to success. The transferability of the paths without also transferring all or most of the circumstances is unknown. This removes food and fertiliser policymaking from the realm of analysis and places it in the realm of judgment, experience, and politics.
FOOD POLICY
February
1976